Competition in electricity markets leads to volatile conditions which cause persistent price fluctuations over time. This study explores the problem of electricity pricing fluctuations in the DE-LU bidding zone from October 2018 to March 2022 by applying time series analysis. The determinants of electricity price fluctuations are broken down into three groups: exogenous prices (gas, coal and CO₂ prices), internal (consumption and generation) and external (net import between neighboring bidding zones) electricity flows. Based on the SARIMAX model, we tried to combine all these factors to forecast electricity prices in the single bidding zone. It was found that the SARIMAX (1, 1, 2) × (3, 1, 0, 7) model with exogenous prices, internal and external electricity flows, which has the lowest AIC and MAPE values, is the best-fitted model for the DE-LU bidding zone. Anonymous trading and unpredictable individual bidding strategies lead to persistent price volatility, which causes electricity prices to deviate from fundamental trends. To reveal the risk factors, the SARIMAX model of electricity prices needs to be supplemented with a GARCH model of the residual returns. For forecasting electricity price residual volatility in the DE-LU bidding zone, the SARIMAX model with exogenous prices, internal and external electricity flows must be accompanied with the GARCH (7, 0) model.

Ensuring the sustainability of the European power system is one of the key priorities in the implementation of the EU’s ambitious plans to become climate-neutral by 2050. The uniqueness of the power systems of the EU member states necessitates their assessment and comparison. The article offers a composite indicator, namely, the power system sustainability index (PSS index), to assess the current level of the development of the power systems via three dimensions (social, economic, and environmental) and eight local indicators: the household electricity consumption per capita; the commercial electricity consumption per GDP; the external dependency of the power system; the energy efficiency of the generation; the capacity utilization factor; the share of organic fossil fuels; the share of renewable energy resources; and the greenhouse gas (GHG) emissions per unit of primary energy source. The “energy mix” is defined as the key impact factor, which has a contradictory effect on the local power system sustainability (LPSS) indicators, which can be represented as a set of regression models. The data of the regression analysis can be used for performing a multiobjective optimization by the local indicators, and they can determine the vectors of change required to ensure the sustainability of the power system. The research results prove that it is possible to minimize the GHG emissions per unit of primary energy source and maximize the energy efficiency of generation, while reducing the capacity utilization and increasing the external dependency of the power system.

Our paper examines the role of green hydrogen in rebuilding Ukraine’s energy sector using a geospatial least-cost modelling approach. Ukraine’s war-damaged, Soviet-era, centralized energy system requires not only reconstruction but structural redesign consistent with EU decarbonization goals. We argue that the destruction of a fossil- and nuclear-heavy electricity system increases the strategic value of decentralized renewables and hydrogen-based flexibility.,The paper assesses hydrogen production costs under four scenarios: a de-risked benchmark (8% WACC), a pre-war financing case (12.2% WACC), a war-risk case (25% WACC), and a learning case combining 8% WACC with moderate technological improvements. Results show that screening-level levelized cost of hydrogen ranges from €3.88-7.28/kg in the 8% case, rises to €5.04-9.60/kg under pre-war financing, and increases sharply to €9.06-17.58/kg under war-risk conditions. With learning and de-risking, costs fall to €3.12-5.09/kg.,Our results reveal that delivered costs are lowest for the Odesa export hub (€4.88/kg) and the Dnipro industrial hub (€4.91/kg), while Zakarpattia cases (€6.09-6.77/kg) remain strategically important under conflict constraints. Our findings show that financing and security risk matter as much as resource quality. Green hydrogen can support post-war reconstruction, but only within a domestic-first, phased strategy focused on resilience, decarbonization, risk mitigation, and selective EU-oriented export development.

The article is devoted to the history and development of regional studies as an interdisciplinary science. This article discusses the concept of regional studies and the history of its formation and development in different countries. Particular attention is paid to works devoted to changes in the development of regional studies in Russia and China. In addition, the article provides a brief overview of the functions of regional studies in Europe and the United States.

Based on the analysis of the modern Chinese-Russian relations, the history of the development of infrastructure and port trade between Russia and China, the author puts forward reasonable hypotheses about the main problems, features and trends in the development of port trade in the Far East and Heilongjiang province.

Our paper systematically studies how artificial intelligence (AI) is represented and applied in research on the energy transition of modern power grids. It combines a network bibliometric review and VOSviewer mapping with metadata-based natural language processing (NLP) of 361 Scopus records published between 2018 and 2026. We attempt to identify the intellectual structure of the field but do not infer that AI causes the energy transition. To connect the dominant forecasting and coordination themes to power-system operation, our study introduces a controlled one-week dispatch experiment in which generation, storage, demand flexibility, reserve requirements, costs, and actual time series are held constantly across scenarios.Titles, abstracts, author keywords, and index keywords were represented using TF-IDF n-grams, non-negative matrix factorization, truncated-SVD embeddings, k-means clustering, and dictionary-based pattern detection. Candidate cluster solutions were compared using the silhouette coefficient, and results were triangulated with the VOSviewer maps. Six interrelated domains emerge: forecasting and predictive analytics; microgrid control and distributed resources; renewable integration, storage, and electric-vehicle charging; cybersecurity and digital infrastructure; policy, markets, and transition governance; and solar/urban applications. In the controlled simulation, only forecast-information quality changes (18%, 11%, and 4% mean absolute percentage error). Across 50 matched Monte Carlo runs, the 4% error case reduces balancing energy by 77.6%, curtailment by 60.5%, and operating cost by 8.0% relative to the 18% case, while emissions decline by 1.2% and renewable share rises by 0.42 percentage points. These results support a narrower conclusion: AI-enabled forecasting may improve operational coordination when embedded in an otherwise unchanged system, but we do not establish AI as a structural or economy-wide determinant of the renewable energy transition.